streamlit_app.py/pages/SentimentDetection.py

from os import path
import streamlit as st

# import pickle

# from tensorflow import keras
import tensorflow as tf
import torch
from torch import nn
from transformers import BertModel, BertTokenizer


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL_NAME = 'bert-base-cased'

# Build the Sentiment Classifier class 
class SentimentClassifier(nn.Module):
    
    # Constructor class 
    def __init__(self, n_classes):
        super(SentimentClassifier, self).__init__()
        self.bert = BertModel.from_pretrained(MODEL_NAME)
        self.drop = nn.Dropout(p=0.3)
        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
    
    # Forward propagaion class
    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(
          input_ids=input_ids,
          attention_mask=attention_mask,
          return_dict=False
        )
        #  Add a dropout layer 
        output = self.drop(pooled_output)
        return self.out(output)

# from keras_preprocessing.sequence import pad_sequences


# def predict(ham_spam):
#     model = load_model(r'test_HSmodel_r.h5')
#     with open('tokenizer.pickle','rb') as handle:
#         tokenizer = pickle.load(handle)
#         tokenizer.fit_on_texts(ham_spam)
#         x_1 = tokenizer.texts_to_sequences([ham_spam])
#         x_1 = pad_sequences(x_1, maxlen=525)
#         predictions = model.predict(x_1)[0][0]
#         return predictions

MODEL_PATH = path.join(path.dirname(__file__), "bert_model.h5")


@st.cache_resource
def load_model_and_tokenizer():
    model = SentimentClassifier(3)
    model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu')))
    model.eval()
    return model, BertTokenizer.from_pretrained('bert-base-cased')


def predict(content):
    model, tokenizer = load_model_and_tokenizer()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    encoded_review = tokenizer.encode_plus(
        content,
        max_length=160,
        add_special_tokens=True,
        return_token_type_ids=False,
        pad_to_max_length=True,
        return_attention_mask=True,
        return_tensors="pt",
    )

    input_ids = encoded_review["input_ids"].to(device)
    attention_mask = encoded_review["attention_mask"].to(device)

    output = model(input_ids, attention_mask)
    _, prediction = torch.max(output, dim=1)

    class_names = ["negative", "neutral", "positive"]

    return class_names[prediction]


def main():
    # giving a title to our page
    st.title("Sentiment detection")
    contents = st.text_area("Please enter reviews/sentiment/setences/contents:")

    prediction = ""

    # Create a prediction button
    if st.button("Analyze Spam Detection Result"):
        prediction = predict(contents)
        st.success(prediction)


if __name__ == "__main__":
    main()